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ML Introduction with scikit-learn
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ML Introduction with scikit-learn

ML Introduction with scikit-learn

1. Machine Learning Concepts
What is MLTypes of Machine LearningTraining SetTypes of DataMachine Learning Workflow
2. Preprocessing Data with Scikit-learn
Scikit-learn ConceptsGetting Familiar with DatasetDealing with Missing ValuesImpute Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderEncode Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerScale the FeaturesPreprocessing Summary
3. Pipelines
What is PipelineColumnTransformerPipelineCreating a Pipeline ChallengeFinal EstimatorCreate a Complete ML Pipeline
4. Modeling
ModelsKNeighborsClassifierEvaluating a Model. Train-Test split.Cross-ValidationEvaluate the Model with Cross-ValidationGridSearchCVThe Flaw of GridSearchCVTune Hyperparameters with RandomizedSearchCVModeling SummaryPutting It All Together

bookCreating a Pipeline Challenge

In this challenge, you need to put all the preprocessing steps we did together into one pipeline.
The dataset is the initial penguins.csv file we started from.
The first step is to remove two useless rows.
Then you will have to create a pipeline containing Encoding, Imputing, and Scaling.

You only need to encode two columns, 'sex' and 'island'. Since you do not want to encode the whole X, you must use ColumnTransformer. Then apply the SimpleImputer and StandardScaler to the entire X.

Here is a reminder of the make_column_transformer() and make_pipeline() functions you will use.

Завдання
test

Swipe to show code editor

  1. Import the correct function for making a pipeline.
  2. Make a ColumnTransformer with the OneHotEncoder applied only to columns 'sex' and 'island'.
  3. Set a remainder argument of make_column_transformer so that all the numerical columns remain untouched.
  4. Make a pipeline containing ct you just created, SimpleImputer with the strategy of 'most_frequent' and a StandardScaler as a last step.
  5. Transform the X using the pipe you created.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

Секція 3. Розділ 4
toggle bottom row

bookCreating a Pipeline Challenge

In this challenge, you need to put all the preprocessing steps we did together into one pipeline.
The dataset is the initial penguins.csv file we started from.
The first step is to remove two useless rows.
Then you will have to create a pipeline containing Encoding, Imputing, and Scaling.

You only need to encode two columns, 'sex' and 'island'. Since you do not want to encode the whole X, you must use ColumnTransformer. Then apply the SimpleImputer and StandardScaler to the entire X.

Here is a reminder of the make_column_transformer() and make_pipeline() functions you will use.

Завдання
test

Swipe to show code editor

  1. Import the correct function for making a pipeline.
  2. Make a ColumnTransformer with the OneHotEncoder applied only to columns 'sex' and 'island'.
  3. Set a remainder argument of make_column_transformer so that all the numerical columns remain untouched.
  4. Make a pipeline containing ct you just created, SimpleImputer with the strategy of 'most_frequent' and a StandardScaler as a last step.
  5. Transform the X using the pipe you created.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

Секція 3. Розділ 4
toggle bottom row

bookCreating a Pipeline Challenge

In this challenge, you need to put all the preprocessing steps we did together into one pipeline.
The dataset is the initial penguins.csv file we started from.
The first step is to remove two useless rows.
Then you will have to create a pipeline containing Encoding, Imputing, and Scaling.

You only need to encode two columns, 'sex' and 'island'. Since you do not want to encode the whole X, you must use ColumnTransformer. Then apply the SimpleImputer and StandardScaler to the entire X.

Here is a reminder of the make_column_transformer() and make_pipeline() functions you will use.

Завдання
test

Swipe to show code editor

  1. Import the correct function for making a pipeline.
  2. Make a ColumnTransformer with the OneHotEncoder applied only to columns 'sex' and 'island'.
  3. Set a remainder argument of make_column_transformer so that all the numerical columns remain untouched.
  4. Make a pipeline containing ct you just created, SimpleImputer with the strategy of 'most_frequent' and a StandardScaler as a last step.
  5. Transform the X using the pipe you created.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

In this challenge, you need to put all the preprocessing steps we did together into one pipeline.
The dataset is the initial penguins.csv file we started from.
The first step is to remove two useless rows.
Then you will have to create a pipeline containing Encoding, Imputing, and Scaling.

You only need to encode two columns, 'sex' and 'island'. Since you do not want to encode the whole X, you must use ColumnTransformer. Then apply the SimpleImputer and StandardScaler to the entire X.

Here is a reminder of the make_column_transformer() and make_pipeline() functions you will use.

Завдання
test

Swipe to show code editor

  1. Import the correct function for making a pipeline.
  2. Make a ColumnTransformer with the OneHotEncoder applied only to columns 'sex' and 'island'.
  3. Set a remainder argument of make_column_transformer so that all the numerical columns remain untouched.
  4. Make a pipeline containing ct you just created, SimpleImputer with the strategy of 'most_frequent' and a StandardScaler as a last step.
  5. Transform the X using the pipe you created.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Секція 3. Розділ 4
Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
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